AstroInsight Research Assistant

### Problem: The identification and classification of pulsar candidates in modern astronomical surveys is a challenging task due to the vast volumes of data, the complexity of the features involved, and the presence of missing data. Existing methods, such as Bayesian networks, automated feature engineering, and deep learning models, have shown promise individually, but their integration has not been thoroughly explored. This integration could enhance the overall efficiency and accuracy of pulsar candidate classification, addressing the limitations of current methods, such as the inability to handle missing data effectively and the reliance on large labeled datasets. ### Rationale: The proposed integrated framework leverages the strengths of Bayesian networks, automated feature engineering, and transformer-based deep learning models. Bayesian networks can model the complex probabilistic relationships between features, while automated feature engineering identifies critical synergistic feature pairs. Transformer-based models, known for their ability to handle sequential and multi-dimensional data, can further improve classification accuracy by leveraging the rich, multi-modal data typical of pulsar candidates. Additionally, time series imputation techniques and semisupervised learning methods are incorporated to handle missing data and leverage unlabeled data effectively. This integrated approach aims to provide a robust and adaptable solution for pulsar candidate classification. ### Necessary technical details: 1. **Bayesian Networks**: To model the probabilistic relationships between different features in pulsar candidate data, leveraging the scalability and uncertainty quantification of Bayesian Stein Networks (BSNs). 2. **Automated Feature Engineering (AutoFE)**: Specifically, algorithms like IIFE to identify synergistic feature pairs, coupled with AutoMAN for efficient feature transform space exploration. 3. **Transformer-based Deep Learning Models**: Vision Transformer (ViT) and Convolutional Vision Transformer (CvT) for classification, integrated with MeMOT for multi-object tracking and handling long-term occlusions. 4. **Time Series Imputation Techniques**: Deep learning methods, such as those used in MeMOT, to handle missing values in the data. 5. **Semisupervised Learning**: Self-tuning pseudolabeling techniques, as discussed in the survey on semisupervised learning, to leverage unlabeled data effectively. ### Datasets: 1. **Fermi Large Area Telescope (LAT) Data**: For high-energy gamma-ray pulsar candidate data. 2. **Pulsar Arecibo L-band Feed Array (PALFA) Survey**: For radio pulsar candidate data. 3. **Green Bank North Celestial Cap (GBNCC) Survey**: For independent validation of the AI model. 4. **Commensal Radio Astronomy FasT Survey (CRAFTS)**: For additional radio pulsar candidate data. 5. **High-Time Resolution Universe (HTRU) Survey**: For testing the classification models. ### Paper title: "Integrated Bayesian Networks, Automated Feature Engineering, and Transformer-based Deep Learning for Enhanced Pulsar Candidate Classification" ### Paper abstract: The identification and classification of pulsar candidates in modern astronomical surveys is a challenging task due to the vast volumes of data and the complexity of the features involved. This paper proposes a novel integrated framework that combines Bayesian networks, automated feature engineering, and transformer-based deep learning models to enhance the accuracy and efficiency of pulsar candidate classification. Bayesian networks are used to model the probabilistic relationships between features, while automated feature engineering identifies critical synergistic feature pairs. Transformer-based deep learning models, including Vision Transformer (ViT) and Convolutional Vision Transformer (CvT), are employed to classify the candidates, leveraging the rich, multi-modal data typical of pulsar surveys. Additionally, time series imputation techniques and semisupervised learning methods are incorporated to handle missing data and leverage unlabeled data effectively. The proposed framework is validated using data from the Fermi LAT, PALFA, GBNCC, CRAFTS, and HTRU surveys, demonstrating significant improvements in classification accuracy and efficiency. This integrated approach represents a significant advancement in the automated identification of pulsar candidates, with potential applications in both current and future astronomical surveys. ### Methods: 1. **Data Preprocessing**: - **Bayesian Networks**: Construct Bayesian networks using Bayesian Stein Networks (BSNs) to model the probabilistic relationships between different features in the pulsar candidate data. - **Automated Feature Engineering**: Apply IIFE algorithms to identify synergistic feature pairs and integrate AutoMAN for efficient feature transform space exploration. - **Time Series Imputation**: Use deep learning-based imputation techniques, similar to those in MeMOT, to handle missing values in the time series data. 2. **Modeling**: - **Transformer-based Deep Learning Models**: Implement Vision Transformer (ViT) and Convolutional Vision Transformer (CvT) models for the classification of pulsar candidates. These models are trained on the enhanced feature sets generated from the Bayesian networks and AutoFE. - **Semisupervised Learning**: Apply self-tuning pseudolabeling techniques to leverage the large volume of unlabeled data, improving the model's generalization capabilities. 3. **Evaluation**: - **Validation**: Use cross-validation techniques to assess the performance of the integrated model. - **Independent Testing**: Validate the model using independent datasets from the GBNCC, CRAFTS, and HTRU surveys. ### Experiments: 1. **Data Preparation**: - **Bayesian Network Construction**: Build Bayesian networks on the Fermi LAT and PALFA datasets to model feature relationships. - **Feature Engineering**: Apply IIFE algorithms to the PALFA and GBNCC datasets to identify synergistic feature pairs. - **Imputation**: Use deep learning-based imputation techniques on the HTRU dataset to handle missing values. 2. **Model Training**: - **Transformer Models**: Train ViT and CvT models on the enhanced feature sets from the Bayesian networks and AutoFE. - **Semisupervised Learning**: Apply self-tuning pseudolabeling techniques to the unlabeled data from the CRAFTS survey.